In image processing, there is a need for tools for an automated quality assessment of different algorithms. Typically, there is interest in a comparison against a gold standard or against a previous version of an algorithm.
In the area of automated organ outlining on medical images like CT or MRI, there is the need for an efficient, robust and comprehensible method of comparing different shapes. For example, algorithms can be used for determining a shape of an organ (e.g., kidney, liver, etc.). It is desirable to assess a quality of the determined shape.
Existing methods for comparing shapes include the Dice similarity measurement and the Hausdorff distance measurement, but they can be inaccurate or at least provide undesirable results. For example, the Dice similarity measurement is affected by the scale of the shapes with a fixed offset, indicating a better match as the shapes increase in size. Also, the metric produced by the Dice similarity measurement is a unitless ratio of similarity, which may be unhelpful for certain types of comparisons. The Hausdorff distance measurement produces a metric with distance units, but the Hausdorff distance measurement can provide misleading results because the process can be dramatically affected by outlying points. Also, the measurement can be difficult to execute properly.
Therefore, it is desirable to provide techniques for addressing these problems.